A Comprehensive Review of Visual-Textual Sentiment Analysis from Social
Media Networks
- URL: http://arxiv.org/abs/2207.02160v2
- Date: Wed, 6 Dec 2023 15:29:02 GMT
- Title: A Comprehensive Review of Visual-Textual Sentiment Analysis from Social
Media Networks
- Authors: Israa Khalaf Salman Al-Tameemi, Mohammad-Reza Feizi-Derakhshi, Saeed
Pashazadeh, Mohammad Asadpour
- Abstract summary: Social media networks have become a significant aspect of people's lives, serving as a platform for their ideas, opinions and emotions.
The analysis of these feelings revealed various applications, including brand evaluations, YouTube film reviews and healthcare applications.
Our study focuses on the field of multimodal sentiment analysis, which examines visual and textual data posted on social media networks.
- Score: 2.048226951354646
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Social media networks have become a significant aspect of people's lives,
serving as a platform for their ideas, opinions and emotions. Consequently,
automated sentiment analysis (SA) is critical for recognising people's feelings
in ways that other information sources cannot. The analysis of these feelings
revealed various applications, including brand evaluations, YouTube film
reviews and healthcare applications. As social media continues to develop,
people post a massive amount of information in different forms, including text,
photos, audio and video. Thus, traditional SA algorithms have become limited,
as they do not consider the expressiveness of other modalities. By including
such characteristics from various material sources, these multimodal data
streams provide new opportunities for optimising the expected results beyond
text-based SA. Our study focuses on the forefront field of multimodal SA, which
examines visual and textual data posted on social media networks. Many people
are more likely to utilise this information to express themselves on these
platforms. To serve as a resource for academics in this rapidly growing field,
we introduce a comprehensive overview of textual and visual SA, including data
pre-processing, feature extraction techniques, sentiment benchmark datasets,
and the efficacy of multiple classification methodologies suited to each field.
We also provide a brief introduction of the most frequently utilised data
fusion strategies and a summary of existing research on visual-textual SA.
Finally, we highlight the most significant challenges and investigate several
important sentiment applications.
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